Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes
| Autor: | James V. Sugai, Wang Gong, Riccardo Di Gianfilippo, Chin-Wei Wang, Yu Leo Lei, Yuning Hao, Yuying Xie, Hom-Lay Wang, Nobuhiko Kamada, William V. Giannobile, Jiaqian Li, Kenneth S. Kornman |
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| Rok vydání: | 2021 |
| Předmět: |
Peri-implantitis
microbiome Medicine (miscellaneous) Machine learning computer.software_genre Prevotella intermedia Regenerative medicine Immunophenotyping Cohort Studies Machine Learning Immune system Risk Factors Humans Medicine Macrophage Microbiome Pharmacology Toxicology and Pharmaceutics (miscellaneous) B-Lymphocytes Fusobacterium nucleatum biology business.industry Macrophages Microbiota immune profiling Regeneration (biology) Th1 Cells biology.organism_classification Peri-Implantitis FARDEEP classification Cytokines Th17 Cells Artificial intelligence business computer Algorithms Research Paper |
| Zdroj: | Theranostics |
| ISSN: | 1838-7640 |
| DOI: | 10.7150/thno.57775 |
| Popis: | Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution. Results: Unsupervised clustering identified risk groups with distinct immune profiles, microbial colonization dynamics, and regenerative outcomes. Low-risk patients exhibited elevated M1/M2-like macrophage ratios and lower B-cell infiltration. The low-risk immune profile was characterized by enhanced complement signaling and higher levels of Th1 and Th17 cytokines. Fusobacterium nucleatum and Prevotella intermedia were significantly enriched in high-risk individuals. Although surgery reduced microbial burden at the peri-implant interface in all groups, only low-risk individuals exhibited suppression of keystone pathogen re-colonization. Conclusion: Peri-implant immune microenvironment shapes microbial composition and the course of regeneration. Immune signatures show untapped potential in improving the risk-grading for peri-implantitis. |
| Databáze: | OpenAIRE |
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